This paper presents our latest effort on improving Code-switching language models that suffer from data scarcity. We investigate methods to augment Code-switching training text data by artificially generating them. Concretely, we propose a cycle-consistent adversarial networks based framework to transfer monolingual text into Code-switching text, considering Code-switching as a speaking style. Our experimental results on the SEAME corpus show that utilising artificially generated Code-switching text data improves consistently the language model as well as the automatic speech recognition performance.
@article{arxiv.2112.06327,
title = {Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks},
author = {Chia-Yu Li and Ngoc Thang Vu},
journal= {arXiv preprint arXiv:2112.06327},
year = {2021}
}